1. Field of the Invention
The present invention relates to an apparatus, a method, and a computer program product for creating learning data for learning word translation according to a context.
2. Description of the Related Art
A machine translation apparatus that translates an input text in a source language (original text) into a text in a target language different from the source language (translated text) includes a bilingual dictionary in which a plurality of combinations of a word in the source language and a translation in the target language is stored. The bilingual dictionary is generally searched, by using a part or an entire original text as a key, to create a translation to be output based on the searched translation.
Even with the same word in the source language, appropriate translation is sometimes different according to an appeared context. Therefore, in the bilingual dictionary, a plurality of translations in the target language is often registered with respect to one word in the source language. Therefore, it is an important issue to select an appropriate translation according to the appeared context from the translations, to improve translation accuracy by the machine translation apparatus.
As one measure with respect to the issue, there has been a method that an appropriate translation is learnt for each appeared context to select a translation by referring to a learning result. For example, JP-A 2002-73602 (KOKAI) proposes a technique for a translation learning method, in which a user specifies an appropriate translation with respect to a word in an original text, for which an inappropriate translation has been output, referring to the original text and the translation output by a machine translation apparatus.
However, according to the translation learning method described in JP-A 2002-73602 (KOKAI), the user needs to instruct an appropriate translation to the system word by word, and therefore a significant amount of labor is required. That is, in the conventional method, because translation learning data for learning the translation for each appeared context is generally created manually, processing load for generating the learning data becomes excessive.